In the mining industry, a framework exists for
quantitative assessment of interpretation uncertainty of spatial
domains used to model a stationary spatial domain for mineral
resource estimation. This framework will improve public
reporting of mineral resource estimates, and improve the
reliability of feasibility studies by ensuring successful
communication of geological risk. In early-stage mineral projects,
there is often not enough multielement laboratory data to enable
the use of calculated geological methods for quantitative
uncertainty assessment. Portable X-Ray Fluoresce (pXRF) is an
accepted method of providing cost and time effective multielement
measurements for early-stage projects. However, these
measurements are of lower precision and accuracy, then
laboratory-based measurements. Recent work has shown that
quantitative uncertainty assessments using a Bayesian
approximation method can successfully use both pXRF and
laboratory data. Subjective visual assessment of uncertainty band
graphs, drill hole plots, and confidence matrices suggest that
models derived from the two types of data provide similar
uncertainty assessments. This paper reviews recent advances in
Null Hypothesis and Bayesian Hypothesis statistical methods for
comparing models to propose a robust methodological framework
for assessing the reliability and similarity of supervised
classification models utilising confusion matrix model metrics for
further research in the use of pXRF as a suitable measurement for
geological spatial domain uncertainty.
Original languageEnglish
Number of pages4
Publication statusPublished - 21 Oct 2020
EventHigher Degree Research (HDR) Conference: School of Computing and Mathematics Faculty of Business, Justice and Behavioural Sciences - zoom
Duration: 21 Oct 202022 Oct 2020


ConferenceHigher Degree Research (HDR) Conference

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